Feasibility study on the optical detection of infrasonic waves based on an acoustically modulated carrier signal
Bibliographic record
Abstract
The detection of a propagating wave usually requires the use of a physical sensor that measures a specific property - displacement, velocity, or pressure. Measurement devices such as hydrophones, microphones, accelerometers and seismometers are routinely used for the characterisation of sonic waves in various media. An interesting question arises, when establishing new measurement methods and standards for the calibration of such sensors, whether it is possible to use a wave, rather than a physical device, for sensing propagating disturbances. In our proposed scheme, we mix an infrasonic wave with a carrier acoustic wave at a fixed audible frequency and amplitude in air. The optical method based on photon correlation makes it possible to measure the carrier acoustic particle velocity at a point in space and thus reconstruct the carrier accoustic wave. Since the measured carrier signal is accoustically modulated by induced infrasonic signals, as well as disturbances such as ambient air flow, this alternative modulation scheme provides interesting and new detection capabilities for propagating infrasonic waves in air. Using a 2 kHz carrier, we experimentally demonstrate the feasibility of this technique for measuring infrasonic signals from 4 to 20 Hz, in relative agreement with a calibrated sound level meter. A systematic discrepency between the two techniques is still being investigated in order to achieve quantitative agreement between the two methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".